Deep-Learning-with-PyTorch

396 CHAPTER 13 Using segmentation to find suspected nodules
This looks very similar to the writer.add_scalar calls we’ve seen before. The dataformats='HWC'
argument tells TensorBoard that the order of axes in our image has
our RGB channels as the third axis. Recall that our network layers often specify outputs
that are B × C × H × W, and we could put that data directly into TensorBoard as
well if we specified 'CHW'.
We also want to save the ground truth that we’re using to train, which will form the
top row of our TensorBoard CT slices we saw earlier in figure 13.16. The code for that
is similar enough to what we just saw that we’ll skip it. Again, check p2ch13/training.py
if you want the details.
13.6.5 Updating our metrics logging
To give us an idea how we are doing, we compute per-epoch metrics: in particular,
true positives, false negatives, and false positives. This is what the following listing
does. Nothing here will be particularly surprising.
Listing 13.31
training.py:400, .logMetrics
sum_a = metrics_a.sum(axis=1)
allLabel_count = sum_a[METRICS_TP_NDX] + sum_a[METRICS_FN_NDX]
metrics_dict['percent_all/tp'] = \
sum_a[METRICS_TP_NDX] / (allLabel_count or 1) * 100
metrics_dict['percent_all/fn'] = \
sum_a[METRICS_FN_NDX] / (allLabel_count or 1) * 100
metrics_dict['percent_all/fp'] = \
sum_a[METRICS_FP_NDX] / (allLabel_count or 1) * 100
Can be larger than 100%
since we’re comparing to
the total number of pixels
labeled as candidate
nodules, which is a tiny
fraction of each image
We are going to start scoring our models as a way to determine whether a particular
training run is the best we’ve seen so far. In chapter 12, we said we’d be using the F1
score for our model ranking, but our goals are different here. We need to make sure
our recall is as high as possible, since we can’t classify a potential nodule if we don’t
find it in the first place!
We will use our recall to determine the “best” model. As long as the F1 score is reasonable
for that epoch, 15 we just want to get recall as high as possible. Screening out
any false positives will be the responsibility of the classification model.
Listing 13.32
training.py:393, .logMetrics
def logMetrics(self, epoch_ndx, mode_str, metrics_t):
# ... line 453
score = metrics_dict['pr/recall']
return score
15 And yes, “reasonable” is a bit of a dodge. “Nonzero” is a good starting place, if you’d like something more
specific.